{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Mean</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>3831732.00</td>\n",
       "      <td>7599295.00</td>\n",
       "      <td>5579519.95</td>\n",
       "      <td>1262194.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>181.54</td>\n",
       "      <td>2110.78</td>\n",
       "      <td>765.04</td>\n",
       "      <td>595.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>448.19</td>\n",
       "      <td>6882.85</td>\n",
       "      <td>2370.83</td>\n",
       "      <td>1919.17</td>\n",
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       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>7571.00</td>\n",
       "      <td>42049.14</td>\n",
       "      <td>19644.69</td>\n",
       "      <td>10203.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x5</th>\n",
       "      <td>6212.70</td>\n",
       "      <td>33156.83</td>\n",
       "      <td>15870.95</td>\n",
       "      <td>8199.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x6</th>\n",
       "      <td>6370241.00</td>\n",
       "      <td>8323096.00</td>\n",
       "      <td>7350513.60</td>\n",
       "      <td>621341.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>525.71</td>\n",
       "      <td>4454.55</td>\n",
       "      <td>1712.24</td>\n",
       "      <td>1184.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x8</th>\n",
       "      <td>985.31</td>\n",
       "      <td>15420.14</td>\n",
       "      <td>5705.80</td>\n",
       "      <td>4478.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x9</th>\n",
       "      <td>60.62</td>\n",
       "      <td>228.46</td>\n",
       "      <td>129.49</td>\n",
       "      <td>50.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x10</th>\n",
       "      <td>65.66</td>\n",
       "      <td>852.56</td>\n",
       "      <td>340.22</td>\n",
       "      <td>251.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x11</th>\n",
       "      <td>97.50</td>\n",
       "      <td>120.00</td>\n",
       "      <td>103.31</td>\n",
       "      <td>5.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x12</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x13</th>\n",
       "      <td>5321.00</td>\n",
       "      <td>41972.00</td>\n",
       "      <td>17273.80</td>\n",
       "      <td>11109.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>64.87</td>\n",
       "      <td>2088.14</td>\n",
       "      <td>618.08</td>\n",
       "      <td>609.25</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            Min         Max        Mean         STD\n",
       "x1   3831732.00  7599295.00  5579519.95  1262194.72\n",
       "x2       181.54     2110.78      765.04      595.70\n",
       "x3       448.19     6882.85     2370.83     1919.17\n",
       "x4      7571.00    42049.14    19644.69    10203.02\n",
       "x5      6212.70    33156.83    15870.95     8199.77\n",
       "x6   6370241.00  8323096.00  7350513.60   621341.85\n",
       "x7       525.71     4454.55     1712.24     1184.71\n",
       "x8       985.31    15420.14     5705.80     4478.40\n",
       "x9        60.62      228.46      129.49       50.51\n",
       "x10       65.66      852.56      340.22      251.58\n",
       "x11       97.50      120.00      103.31        5.51\n",
       "x12        1.03        1.91        1.42        0.25\n",
       "x13     5321.00    41972.00    17273.80    11109.19\n",
       "y         64.87     2088.14      618.08      609.25"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "inputfile = '../data/data1.csv' #输入的数据文件\n",
    "data = pd.read_csv(inputfile) #读取数据\n",
    "r = [data.min(), data.max(), data.mean(), data.std()] #依次计算最小值、最大值、均值、标准差\n",
    "r = pd.DataFrame(r, index = ['Min', 'Max', 'Mean', 'STD']).T  #计算相关系数矩阵\n",
    "np.round(r, 2) #保留两位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.91</td>\n",
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       "    <tr>\n",
       "      <th>x5</th>\n",
       "      <td>0.97</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
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       "    <tr>\n",
       "      <th>x6</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>-0.34</td>\n",
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       "      <td>0.94</td>\n",
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       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
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       "    <tr>\n",
       "      <th>x8</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.98</td>\n",
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       "      <td>0.99</td>\n",
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       "      <td>0.98</td>\n",
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       "    <tr>\n",
       "      <th>x10</th>\n",
       "      <td>0.98</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
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       "    <tr>\n",
       "      <th>x11</th>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x12</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x13</th>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "       x1    x2    x3    x4    x5    x6    x7    x8    x9   x10   x11   x12  \\\n",
       "x1   1.00  0.95  0.95  0.97  0.97  0.99  0.95  0.97  0.98  0.98 -0.29  0.94   \n",
       "x2   0.95  1.00  1.00  0.99  0.99  0.92  0.99  0.99  0.98  0.98 -0.13  0.89   \n",
       "x3   0.95  1.00  1.00  0.99  0.99  0.92  1.00  0.99  0.98  0.99 -0.15  0.89   \n",
       "x4   0.97  0.99  0.99  1.00  1.00  0.95  0.99  1.00  0.99  1.00 -0.19  0.91   \n",
       "x5   0.97  0.99  0.99  1.00  1.00  0.95  0.99  1.00  0.99  1.00 -0.18  0.90   \n",
       "x6   0.99  0.92  0.92  0.95  0.95  1.00  0.93  0.95  0.97  0.96 -0.34  0.95   \n",
       "x7   0.95  0.99  1.00  0.99  0.99  0.93  1.00  0.99  0.98  0.99 -0.15  0.89   \n",
       "x8   0.97  0.99  0.99  1.00  1.00  0.95  0.99  1.00  0.99  1.00 -0.15  0.90   \n",
       "x9   0.98  0.98  0.98  0.99  0.99  0.97  0.98  0.99  1.00  0.99 -0.23  0.91   \n",
       "x10  0.98  0.98  0.99  1.00  1.00  0.96  0.99  1.00  0.99  1.00 -0.17  0.90   \n",
       "x11 -0.29 -0.13 -0.15 -0.19 -0.18 -0.34 -0.15 -0.15 -0.23 -0.17  1.00 -0.43   \n",
       "x12  0.94  0.89  0.89  0.91  0.90  0.95  0.89  0.90  0.91  0.90 -0.43  1.00   \n",
       "x13  0.96  1.00  1.00  1.00  0.99  0.94  1.00  1.00  0.99  0.99 -0.16  0.90   \n",
       "y    0.94  0.98  0.99  0.99  0.99  0.91  0.99  0.99  0.98  0.99 -0.12  0.87   \n",
       "\n",
       "      x13     y  \n",
       "x1   0.96  0.94  \n",
       "x2   1.00  0.98  \n",
       "x3   1.00  0.99  \n",
       "x4   1.00  0.99  \n",
       "x5   0.99  0.99  \n",
       "x6   0.94  0.91  \n",
       "x7   1.00  0.99  \n",
       "x8   1.00  0.99  \n",
       "x9   0.99  0.98  \n",
       "x10  0.99  0.99  \n",
       "x11 -0.16 -0.12  \n",
       "x12  0.90  0.87  \n",
       "x13  1.00  0.99  \n",
       "y    0.99  1.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#皮尔森相关系数是衡量线性关联性的程度---连续变量\n",
    "np.round(data.corr(method = 'pearson'), 2) #计算相关系数矩阵，保留两位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.         0.05745012 0.         0.         0.\n",
      " 0.29140935 0.         0.         0.         0.         0.\n",
      " 0.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso,LassoLarsIC\n",
    "#对套索回归 常数乘以L1项，使用L2范数来归一化\n",
    "reg = LassoLarsIC(criterion='aic')\n",
    "reg.fit(data.iloc[:,0:13],data['y'])\n",
    "#model = Lasso(alpha=1,normalize=True)\n",
    "#model.fit(data.iloc[:,0:13],data['y'])\n",
    "print(reg.coef_)\n",
    "#print(model.coef_) #各个特征的系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x3后验差比值：0.1239\n",
      "x5后验差比值：0.0447\n",
      "x7后验差比值：0.1048\n"
     ]
    }
   ],
   "source": [
    "#-*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from GM11 import GM11 #引入自己编写的灰色预测函数\n",
    "\n",
    "inputfile = '../data/data1.csv' #输入的数据文件\n",
    "outputfile = '../tmp/data1_GM11.xls' #灰色预测后保存的路径\n",
    "outputfile = '../tmp/data1_GM12.xls'\n",
    "data = pd.read_csv(inputfile) #读取数据\n",
    "data.index = range(1994, 2014)\n",
    "\n",
    "data.loc[2014] = None\n",
    "data.loc[2015] = None\n",
    "l = ['x1', 'x2', 'x3', 'x4', 'x5', 'x7']\n",
    "l1 = ['x3','x5','x7']\n",
    "for i in l1:\n",
    "  f,_,_,_,C,_ = GM11(data[i].loc[range(1994,2014)].values)\n",
    "  print(\"%s后验差比值：%0.4f\"%(i,C))\n",
    "  data[i].loc[2014] = f(len(data)-1) #2014年预测结果\n",
    "  data[i].loc[2015] = f(len(data)) #2015年预测结果\n",
    "  data[i] = data[i].round(2) #保留两位小数\n",
    "\n",
    "data[l1+['y']].to_excel(outputfile) #结果输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\software\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
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    {
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     "text": [
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     "text": [
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#-*- coding: utf-8 -*-\n",
    "import pandas as pd\n",
    "inputfile = '../tmp/data1_GM11.xls' #灰色预测后保存的路径\n",
    "#inputfile1 = '../tmp/data1_GM12.xls'\n",
    "outputfile = '../data/revenue11.xls' #神经网络预测后保存的结果\n",
    "#outputfile1 = '../data/revenue12.xls'\n",
    "modelfile = '../tmp/1-net.model' #模型保存路径\n",
    "data = pd.read_excel(inputfile) #读取数据\n",
    "#feature = ['x1', 'x2', 'x3', 'x4', 'x5', 'x7'] #特征所在列\n",
    "feature = ['x3','x5','x7']\n",
    "\n",
    "data_train = data.loc[range(1994,2014)].copy() #取2014年前的数据建模\n",
    "data_mean = data_train.mean()\n",
    "data_std = data_train.std()\n",
    "data_train = (data_train - data_mean)/data_std #数据标准化\n",
    "x_train = data_train[feature].values #特征数据\n",
    "y_train = data_train['y'].values #标签数据\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "\n",
    "model = Sequential() #建立模型\n",
    "model.add(Dense(12,activation='relu',input_dim=3))\n",
    " #用relu函数作为激活函数，能够大幅提供准确度\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam') #编译模型\n",
    "model.fit(x_train, y_train, epochs = 10000, batch_size = 16,verbose=2) #训练模型，学习一万次\n",
    "model.save_weights(modelfile) #保存模型参数\n",
    "\n",
    "#预测，并还原结果。\n",
    "x = ((data[feature] - data_mean[feature])/data_std[feature]).values\n",
    "data[u'y_pred'] = model.predict(x) * data_std['y'] + data_mean['y']\n",
    "data.to_excel(outputfile)\n",
    "\n",
    "import matplotlib.pyplot as plt #画出预测结果图\n",
    "p = data[['y','y_pred']].plot(subplots = True, style=['b-o','r-*'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "file = '../data/revenue12.xls'\n",
    "\n",
    "p = pd.read_excel(file)\n",
    "p = p[['y','y_pred']].copy()\n",
    "p.index=range(1994,2016)\n",
    "import matplotlib.pylab as plt\n",
    "#使用pandas的画图函数作图\n",
    "p.plot(style=['b-o','r-*'],xticks=p.index,figsize=(15,5))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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